The Wheat Bugs (Insecta Heteroptera) in Spanish Wheat Samples, and Their Effect on Flour Rheological Quality

Abstract

The Sunn Pest belong to genera Eurygaster and Aelia. Their feeding activity changes protein quality, due to an enzyme complex (proteases, lipases, alfa and beta amylases, and invertase) present in the bug's saliva. In Spain, the effects of Sunn Pests cause important economic losses, either by crop reduction or by the resulting low quality flour, which impedes its commercialisation. As a consequence, damaged wheat must be mixed with high quality wheat, or the flour must be chemically treated to overcome baking defects. Our objectives were to update information on Sunn Pests in Spain, to try to relate their presence to wheat damage, and to develop a model to minimize the consequences of that damage. Samples studied were those taken for the annual inquiry of the quality of wheat, promoted by the Spanish Association of Cereal Technicians. Samples amount to 96.5% of the cropped area, and to 97.1% of the total production. The period of study covers the last ten years, and includes about 700 samples of soft wheat/year. The results show that about a 15% of the studied samples were infested by Sunn Pests, with an average density of 0.20 bugs/sample. In all, 7 species of Sunn Pests where recorded, unevenly distributed among wheat varieties and geographic areas in Spain. Aelia germari and A. rostrata were considered the most harmful species. Alveographic %W of dough after a 2 h resting show statistical significant differences (p<0,05) when samples with and without Sunn Pests are compared. Infested wheat samples have a high proteolytic activity, which may be a good indication to detect wheat samples affected by degradation. To help provide the wheat industry with a quick screening method at the reception of wheat samples, a computational model, based on artificial neural nets and logistic regression, has been developed. The model is based on entomological data, visible damage on wheat grains, and the usual analytical parameters. It may predict 78.33% of the cases (>25% W at 2 h resting).